Abstract
Provisioning of virtual machines for efficient geospatial query management on cloud is an interesting and challenging work. The aim of this paper is to distribute workloads of different types of spatial queries into suitable virtual machine efficiently. To increase the effectiveness of the system serving geospatial queries, we use real-time geospatial query pattern learning methodology. This methodology is used to train the application specific properties, and the system will learn which type of the geospatial query should be allocated to what type of virtual machine automatically. The learning methodology gives knowledge about the resource required by each type of geospatial query. Using this understanding, various geospatial query templates are stored in the query template repository for further assistance. By this way, fast and robust assignment of virtual machine for the geospatial queries is possible which reduces their waiting time.
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References
Dastjerdi, A.V., Buyya, R.: An autonomous time-dependent SLA negotiation strategy for cloud computing. Comput. J. 58(11), 3202–3216 (2015)
Mansouri, Y., Toosi, A.N., Buyya, R.: Cost optimization for dynamic replication and migration of data in cloud data centers. IEEE Trans. Cloud Comput. (2017)
Chi, Y., Moon, H.J., Hacigümüş, H., Tatemura, J.: SLA-tree: a framework for efficiently supporting SLA-based decisions in cloud computing. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 129–140. ACM (2011)
Chi, Y., Moon, H.J., Hacigümüş, H.: iCBS: incremental cost-based scheduling under piecewise linear sLAS. Proc. VLDB Endow. 4(9), 563–574 (2011)
Jalaparti, V., Ballani, H., Costa, P., Karagiannis, T., Rowstron, A.: Bridging the tenant-provider gap in cloud services. In: Proceedings of the Third ACM Symposium on Cloud Computing, p. 10. ACM (2012)
Liu, Z., Hacıgümüş, H., Moon, H.J., Chi, Y., Hsiung, W.P.: PMAX: tenant placement in multitenant databases for profit maximization. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 442–453. ACM (2013)
Das, J., Dasgupta, A., Ghosh, S.K., Buyya, R.: A geospatial orchestration framework on cloud for processing user queries. In: IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), pp. 1–8. IEEE (2016)
Marcus, R., Papaemmanouil, O.: WiSeDB: a learning-based workload management advisor for cloud databases. Proc. VLDB Endow. 9(10), 780–791 (2016)
Peha, J.M., Tobagi, F.A.: Cost-based scheduling and dropping algorithms to support integrated services. IEEE Tran. Commun. 44(2), 192–202 (1996)
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Das, J., Dasgupta, A., Ghosh, S.K., Buyya, R. (2019). A Learning Technique for VM Allocation to Resolve Geospatial Queries. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_61
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DOI: https://doi.org/10.1007/978-981-10-8639-7_61
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